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02 May 2014
Finding words that sound alike but are spelled wildly differently

I’ve been working on search stuff lately, and we needed some wordlists to help test search results that match only because they sound similar to the query, and not because they’re spelled similarly.

Turns out we couldn’t find a pre-existing wordlist of homophones (words that sound the same but are spelled differently) that are dramatically different in spelling. And our QA team especially wanted some examples of people’s names that meet those criteria.

So, sure, I figured that’d be fun and quick to throw together for them!

It’s a lot like finding anagrams - the basic structure was a dict (a hash map, for the non-Python folks reading this) keyed by the phonetic encoding of each word. Each key pointed to a nested dict, which included an array of words which phonetically matched the key and a bool indicating whether it fit my criteria or not. In the end, all matching words were spit into stdout as a list of comma-separated homophones.

I determined whether words were spelled differently enough by checking whether a small enough percentage of their trigrams were the same. (I also had a minumum length set, so I’d be sure to have enough trigrams per word to be worth checking for match percentage.

(It was kinda neat to find something that felt more like an interview puzzle than anything else, but was actually useful for my day job. Oh hey, look, those skills are occasionally actually useful! Now you don’t have to feel weird about all the time you spent learning how to solve these sorts of puzzles!)

Sweet and simple and fun! Here’s my script and a few of the wordlists I created with it, since I figure other people may also find this sort of thing useful when testing search implementations. (FYI, if you’re using something other than a metaphone/doublemetaphone soundalike algorithm and trigrams for misspellings, you may want to make some adjustments.)

03 Apr 2012
Nantucket: an accidental limerick detector

How can you not name an accidental limerick detector Nantucket? I think there may be laws about that. Point being, I wrote a program that takes any text and tries to find any accidental limericks that might be hiding within (based on syllable counts and rhyme, ignoring punctuation and intent).

Limericks have a fairly loose form. The rhyme scheme is always AABBA, but the syllable count can be anything along the lines of 7-or-8-or-9/7-or-8-or-9/5-or-6/5-or-6/7-or-8-or-9. And as if that weren’t loosey-goosey enough, they can have either anapaestic meter (duh-duh-DUM, duh-duh-DUM) or amphibrachic meter (duh-DUM-duh, duh-DUM-duh)!

So, rather than write a limerick detector that looks for anything even remotely resembling a limerick, I chose the following as the canonical example to work from (at least for now):

There was a young student from Crew
Who learned how to count in base two.
His sums were all done
With zero and one,
And he found it much simpler to do.

Going by the canonical example above, Nantucket is set to look for limericks that are AABBA (rhyme scheme) and 8/8/5/5/9 (syllable count per line). It currently ignores meter, but I may add that requirement in later. It also only looks at words through an American English accent.

How to implement such a thing? Well, I started simple - I used the CMU Pronouncing Dictionary (“cmudict”) as accessed through NLTK (Python’s natural language toolkit) to get each word’s syllable count and pronunciation, and just had Nantucket give up on any potential limericks that included words not in cmudict. It tokenizes the text, then walks through each word in the text in turn, checks to see if there’s a limerick starting with that word, and gives up if it hits a word it can’t analyze or a limerick form violation (such as a word that overflows the end of a limerick line, or a rhyme scheme problem).

(It turned out I had to know Python or Java for the Stanford Natural Language Processing class I started two weeks ago. Er. Okay! This came in very handy as soon as I decided to build Nantucket last week - apparently Python has better natural language processing (“NLP”) libraries than Ruby, so it was absolutely the right choice for this project.) (I want Python’s scientific libraries, but with RSpec. Is that too much to ask?)

So, that was pretty fast to throw together. I defined a rhyme as being any pair of words which have identical last vowel sound plus all sounds following the last vowel sound. That’s not precisely right in American English, but it’s pretty damn close. This was easy to do with cmudict, because every vowel phoneme in cmudict ends with a digit denoting stress, but consonant phonemes never include digits.

So, that was a long bit of geekery. Time for a poetry break!

from Proust’s Swann’s Way:

bad conduct should deserve Was I
then not yet aware that what I
felt myself for her
depended neither
upon her actions nor upon my

was wonderful to another
How I should have loved to We were
unfortunate to
a third Yes if you
like I must just keep in the line for

to abandon the habit of
lying Even from the point of
view of coquetry
pure and simple he
had told her can’t you see how much of

That was a fun start, but I really wanted to handle the tons of words that just aren’t in cmudict. I tested Nantucket on James Joyce’s Ulysses repeatedly throughout this process, to come up with fantastic lists of words I was failing to catch at every step along the way.

I like to break projects down into small enough chunks that I get a sense of accomplishment frequently enough to keep myself motivated to keep on pushing forward. Here, words naturally fell into two categories - words which land in the middle of a line in a potential limerick, and words which land at the end of a line of a potential limerick. Syllable count matters for both, but I only really care about pronunciation for words that have to rhyme.

Next step was to create a function that determines the approximate number of syllables in any given word. A syllable contains a vowel sound, which can map to anywhere from 0 to 3 vowel graphemes (letters, not sounds) (aeiouy). I figured I could count the vowel grapheme groups, add 1 for each grouping of more than one vowel grapheme that isn’t a common digraph (group of graphemes that generally signifies a single phoneme (sound)), add 1 for the common apostrophe-in-place-of-vowel instances, and subtract 1 for the circumstances where ‘ed’, ‘es’, or ‘e’ are likely silent at the end of a word, and get pretty close.

def approx_nsyl(word):
   digraphs = ["ai", "au", "ay", "ea", "ee", "ei", "ey", "oa", "oe", "oi", "oo", "ou", "oy", "ua", "ue", "ui"]
   # Ambiguous, currently split: ie, io
   # Ambiguous, currently kept together: ui
   digraphs = set(digraphs)
   count = 0
   array = re.split("[^aeiouy]+", word.lower())
   for i, v in enumerate(array):
      if len(v) > 1 and v not in digraphs:
         count += 1
      if v == '':
         del array[i]
   count += len(array)
   if re.search("(?⟨=\w)(ion|ious|(?⟨!t)ed|es|[^lr]e)(?![a-z']+)", word.lower()):
      count -= 1
   if re.search("'ve|n't", word.lower()):
      count += 1
   return count

With that in place, I had Nantucket keep going when it came across non-cmudict words that fell in the middle of potential limerick lines, but give up on any potential limerick that hit a non-cmudict word that would fall at the end of a limerick line and have to rhyme. Better, but not good enough. I needed a way to figure out when words not in cmudict rhyme!

Grapheme-to-phoneme conversion (“g2p”), or converting a list of letters into a corresponding list of sounds, is a fascinating and non-trivial problem. I spent a lot of time falling down the rabbit hole of reading intriguing papers on how to design machine-learning algorithms that can take context into account when doing g2p. There are two big problems with g2p in American English. First, there’s the issue of alignment - there just isn’t a consistent correspondence between the number of graphemes and the number of phonemes. And second, there’s the issue of context - the same grapheme (or group of graphemes) can correspond to a different phoneme depending on the other graphemes in the word (think about the “pol” in “politics” (ah) versus “political” (uh)), or even whether the word is a verb or a noun (the “de” in “defect”, for instance (dee versus deh)).

At a glance, I figured I could do a rough pass at the problem by creating a last-syllable dictionary based on cmudict, which I promptly did. It was actually surprisingly helpful, but still missed a lot of words, and wasn’t as accurate as I wanted it to be. (More on this later.) So, I kept thinking and reading about the problem.

Poetry break!

from James Joyce’s Ulysses:

grace about you I can give you
a rare old wine that’ll send you
skipping to hell and
back Sign a will and
leave us any coin you have If you

then he tipped me just in passing
but I never thought hed write making
an appointment I
had it inside my
petticoat bodice all day reading

meant till he put his tongue in my
mouth his mouth was sweetlike young I
put my knee up to
him a few times to
learn the way what did I tell him I

The most promising idea I came across is implemented as free software already as Sequitur (that link links to the paper it’s based on, but you can also find a a free pre-publication copy of the manuscript here). It uses expectation maximization and viterbi training to create a model for g2p for any language, given a suitable dictionary to train from first. (It argues that it’s method is better than other methods I came across earlier, which generally involved hidden markov models or, in the simplest promising paper I read, going from right to left and looking at three graphemes to either side for context).

So, I installed Sequitur and gave it a whirl, training and testing it with huge portions of cmudict. It took hours to train, though, and I ultimately wasn’t thrilled with its accuracy. (To be fair, I only tested its accuracy overall, not its accuracy for last syllables only).

It occurred to me during this process that my needs were actually simpler than that. I don’t need to be able to do g2p for complete words in order to have a functional limerick detector. I only need to do g2p for the last syllable of each end-of-limerick-line word. That simplifies my alignment problem right off the bat, because I start by aligning my grapheme list and my phoneme list to the right (as in the second paper I link to above), and I don’t go far enough to the left for them to have much opportunity to become misaligned to any relevant extent. It also resolves a large portion of my context problem - there’s less flexibility in last-syllable graphones (pairs of graphemes and phonemes) than in entire-word graphones.

As completely fascinated by machine learning algorithms as I am, I decided to go back to the last-syllable dictionary I’d created and see if I could attack the problem by improving its accuracy instead.

I created it by going through every word in cmudict and pulling it out what looked like the last syllable worth of graphemes and the last syllable worth of phonemes, catching the graphemes with:

graphemes = re.search("((?i)[BCDFGHJKLMNPQRSTVWXZ]{1,2}[AEIOUY]+[BCDFGHJKLMNPQRSTVWXZ]*(E|ED)?('[A-Z]{1,2})?)(?![a-zA-Z]+)", word).group()

And catching the phonemes with:

val = min(vals, key=len)
i = -1
while i >= 0 - len(val):
  if isdigit(val[i][-1]):
    str = " ".join(val[i:])

To improve my accuracy, after a few iterations I chose to grab up to 2 consonants prior to the my best estimate of the last vowel sound in the word, and include copies without the first and without either, in my list. For example:

clotted AH0 D
lotted AH0 D
otted AH0 D
ders ER0 Z
ers ER0 Z
rs ER0 Z

This gave me gleeful flashbacks to a childhood parlor trick that my brothers and I can still all do in unison, where we chant at blazingly fast top speed: “everybody-verybody-erybody-rybody-ybody-body-ody-dy-y!”

Poetry break!

from Dostoevsky’s The Brothers Karamazov:

eyes with a needle I love you
I love only you Ill love you
in Siberia
Why Siberia
Never mind Siberia if you

are children of twelve years old who
have a longing to set fire to
something and they do
set things on fire too
Its a sort of disease Thats not true

and be horror struck How can I
endure this mercy How can I
endure so much love
Am I worthy of
it Thats what he will exclaim Oh I

Anyways, once I had modified set of last syllable graphones (pairs of letter lists and sound lists), I used some sweet little command line tools to sort the results into a list of unique types with frequency counts, like so:

sort < suff_a.txt | uniq -c | sort -nr > suff_b.txt

My last step in creating my cmudict-based last-syllable dictionary (“suffdict”) was to keep only the most likely set of phonemes for each unique set of graphemes, and reformat the list to match cmudict’s format so I could just use NLTK’s cmudict corpus reader for my suffdict as well. I did that like so:

def most_prob(file):
   uniq_suffs = []
   goal = open('suff_c.txt', 'a')
   with open(file) as f:
      for line in f:
         suff = re.search("\s[a-zA-Z']+\s", line).group()
         if suff not in uniq_suffs:
            new_line = re.sub("\d+\s(?=[a-z])", "", line)
            new_line = re.sub("(?<=[a-z])\s(?=[A-Z])", " 1 ", new_line).strip()
            goal.write(new_line + '\n')

The above code catches only the first instance of any given grapheme set, which gives me the most probable instance, because I’d already sorted everything in order of highest to lowest number of occurrences.

Now when checking for last-syllable phonemes for a word not in cmudict, I use the same regex I used when creating suffdict to check whether the last syllable worth of graphemes from that novel word is in suffdict, and if so, return the corresponding last syllable worth of phonemes from suffdict. If not, try without the first letter, or without the s or ’s at the end.

Fantastic! I was then able to test my accuracy by running every word in cmudict through cmudict and through my suffdict, and then seeing whether the resulting phoneme lists rhymed.

Poetry break!

from Mark Twain’s Huckleberry Finn:

he suspicion what we’re up to
Maybe he won’t But we got to
have it anyway
Come along So they
got out and went in The door slammed to

and see her setting there by her
candle in the window with her
eyes towards the road and
the tears in them and
I wished I could do something for her

I went through a few iterations of tweaking my suffdict creation method to eke a few extra percentage points of accuracy out of it.

My first attempt, which looked at every possible pronunciation for every word in cmudict when creating my suffdict, gave me 80.29% accuracy.

Next, I realized that since I was always using the shortest possible pronunciation when running words through cmudict, I should probably only look at shortest possible pronunciations when creating my suffdict, for consistency’s sake. That brought me up to 82.20% accuracy.

After that, it occurred to me that if I included up to 2 consonants at the beginning of each last-syllable grapheme list in suffdict instead of just 1, I would have a bit more context and catch a few more words correctly. Which I did! That got me up to 85.78% accuracy.

This was getting pretty exciting, but still not as good as I wanted it to be. I thought about what I was doing, and decided to revisit the function I’d written that checks whether lists of phonemes (in the cmudict-style, but from any source) rhyme. Maybe the problem was there instead.

And, oh, yes! My rhyme_from_phonemes function was checking to see whether the last vowel phoneme and all phonemes thereafter were identical in both words - really, truly identical, that is. It disqualified even pairs of words that had exactly the same sounds but different stress patterns. This might make sense if I were paying attention to meter or defining rhyming differently, but it wasn’t really what I was going for here at all. So, I rewrote that function to ignore the digit of the last vowel phoneme (which denotes stress only) and instead check whether it and all following phonemes were otherwise identical, like so:

def rhyme_from_phonemes(list1, list2):
   i = -1
   while i >= 0 - len(list1):
      if isdigit(list1[i][-1]):
         if i >= 0 - len(list2) and list1[i][:-1] == list2[i][:-1] and (i == -1 or list1[i + 1:] == list2[i + 1:]):
            return True
            return False
      i -= 1

That brought me up to 90.85% accuracy.

That feels pretty good, for my purposes!

I was now catching limericks with novel words at the end of lines. Poetry break time!

from Genesis:

in the iniquity of the
city And while he lingered the
men laid hold upon
his hand and upon
the hand of his wife and upon the

Amorite and the Girgasite
And the Hivite and the Arkite
and the Sinite And
the Arvadite and
the Zemarite and the Hamathite

I took a moment after that to make the basic limerick-finding algorithm a bit faster. My first draft was intentionally simple but inefficient, in that it started fresh for each word in the text, instead of saving the syllable counts and phonemes for words checked on previous limerick attempts. It had to re-analyze a word each time that word was encountered.

That worked well enough to let me get to the interesting g2p problem quickly, but once I was reasonably satisfied with my suffdict, I wanted to refactor to make the whole thing more efficient. The current version holds the phonemes and syllable count of each word encountered in a dict, so it can grab them quickly from that dict the next time they’re encountered instead of having to figure them out from scratch again and again as it goes through the text.

I have some thoughts on increasing the efficiency further (by having it skip forward more intelligently whenever it hits a word it can’t find phonemes for, for instance), but really, it’s at a good enough stage that I wanted to share some accidental limericks with you all already!